Emotional analysis in human-computer interaction based on BP neural network algorithm

In addition to students’ cognitive processes, language learning also includes students’ emotional responses. Emotional factors have a positive role in promoting students’ language learning ability. Language expression contains personal emotions, grasps the emotional factors of oral language, designs a suitable teaching atmosphere, and promotes students’ oral language learning ability through oral communication. It has become unrealistic to use traditional manual judgment of the emotional tendencies contained in speech. Therefore, it is necessary to automatically analyze speech signals through technologies such as machine learning and deep neural networks. In recent years, speech signal feature analysis based on BP neural network method has been widely used in other fields. Based on this, this paper takes the spoken language expressed by students in human-computer dialogue of higher vocational students as the research object to build an emotion analysis model based on BP neural network, determine the evaluation indicators and weights through the analytic hierarchy process, and finally provide measures to be taken in daily teaching to improve the oral learning ability of higher vocational students.